Abstract
In this paper we propose a novel super-resolution based algorithm for the pansharpening of multispectral images. Within the Bayesian formulation, the proposed methodology incorporates prior knowledge on the expected characteristics of multispectral images; that is, it imposes smoothness within each band by means of the energy associated with the ℓ1 norm of vertical and horizontal first order differences of image pixel values and also takes into account the correlation among the bands of the multispectral image. The observation process is modeled using the sensor characteristics of both panchromatic and multispectral images. The method is tested on real and synthetic images, compared with other pansharpening methods, and the quality of the results assessed both qualitatively and quantitatively.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 509-523 |
| Number of pages | 15 |
| Journal | Journal of Signal Processing Systems |
| Volume | 65 |
| Issue number | 3 |
| DOIs | |
| State | Published - Dec 2011 |
Funding
This work has been supported by the “Comisión Nacional de Ciencia y Tecnología” under contract TIN2007-65533 and the Consejería de Innovación, Ciencia y Empresa of the Junta de Andalucía under contracts P07-TIC-02698 and P07-FQM-02701.
Keywords
- Bayesian approach
- Interband correlations
- Multispectral images
- Pansharpening
- Super-resolution
- Variational methods
- ℓ1 image models
ASJC Scopus subject areas
- Control and Systems Engineering
- Theoretical Computer Science
- Signal Processing
- Information Systems
- Modeling and Simulation
- Hardware and Architecture